Expanding the BBMRI-ERIC Directory into a Global Catalogue of COVID-19–Ready Collections: A Joint Initiative of BBMRI-ERIC and ISBER
Bibliographic record
Abstract
Biopreservation and BiobankingVol. 18, No. 5 ISBER CornerExpanding the BBMRI-ERIC Directory into a Global Catalogue of COVID-19–Ready Collections: A Joint Initiative of BBMRI-ERIC and ISBERDaniel R. Catchpoole, Francesco Florindi, Caitlin Ahern, Debra Leiolani Garcia, Piper Mullins, Esther Van Enckevort, Andy Zaayenga, Michaela Th. Mayrhofer, and Petr HolubDaniel R. CatchpooleAddress correspondence to: Daniel R. Catchpoole, PhD, FFSc(RCPA), Tumour Bank, CCRU, Kids Research, The Children's Hospital at Westmead, Locked Bag 4001, Westmead, NSW 2145, Australia E-mail Address: [email protected]Tumour Bank, CCRU, Kids Research, The Children's Hospital at Westmead, Westmead, Australia.Search for more papers by this author, Francesco FlorindiBBMRI-ERIC, Graz, Austria.Search for more papers by this author, Caitlin AhernBBMRI-ERIC, Graz, Austria.Search for more papers by this author, Debra Leiolani GarciaPrivate Consultant, San Mateo, California, USA.Search for more papers by this author, Piper MullinsPan-Smithsonian Cryo-Initiative, Washington, District of Columbia, USA.Search for more papers by this author, Esther Van EnckevortUniversity Medical Centre, Groningen, The Netherlands.Search for more papers by this author, Andy ZaayengaSmarterLab, Martinsville, New Jersey, USA.Search for more papers by this author, Michaela Th. MayrhoferBBMRI-ERIC, Graz, Austria.Search for more papers by this author, and Petr HolubBBMRI-ERIC, Graz, Austria.Search for more papers by this authorPublished Online:29 Sep 2020https://doi.org/10.1089/bio.2020.29075.drcAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookXLinked InRedditEmail View article"Expanding the BBMRI-ERIC Directory into a Global Catalogue of COVID-19–Ready Collections: A Joint Initiative of BBMRI-ERIC and ISBER." Biopreservation and Biobanking, 18(5), pp. 479–480FiguresReferencesRelatedDetailsCited byIntegrating research infrastructures into infectious diseases surveillance operations: Focus on biobanksBiosafety and Health, Vol. 4, No. 6How Many Health Research Biobanks Are There? Sheila O'Donoghue, Simon Dee, Jennifer A. Byrne, and Peter Hamilton Watson13 June 2022 | Biopreservation and Biobanking, Vol. 20, No. 3Basic principles of biobanking: from biological samples to precision medicine for patients13 July 2021 | Virchows Archiv, Vol. 479, No. 2Coronavirus and Biobanking: The Collective Global Experiences of the First Wave and Bracing During the Second Marianne K. Henderson and Zisis Kozlakidis15 December 2020 | Biopreservation and Biobanking, Vol. 18, No. 6 Volume 18Issue 5Oct 2020 InformationCopyright 2020, Mary Ann Liebert, Inc., publishersTo cite this article:Daniel R. Catchpoole, Francesco Florindi, Caitlin Ahern, Debra Leiolani Garcia, Piper Mullins, Esther Van Enckevort, Andy Zaayenga, Michaela Th. Mayrhofer, and Petr Holub.Expanding the BBMRI-ERIC Directory into a Global Catalogue of COVID-19–Ready Collections: A Joint Initiative of BBMRI-ERIC and ISBER.Biopreservation and Biobanking.Oct 2020.479-480.http://doi.org/10.1089/bio.2020.29075.drcPublished in Volume: 18 Issue 5: September 29, 2020Online Ahead of Print:September 15, 2020 TopicsCOVID-19 PDF download
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.050 | 0.054 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.242 | 0.302 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".